BigD233333/D2DF

From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting

Zizhao Chen, Ping Wei^†, Guang Dai, Jingdong Wang, Mengmeng Wang^†

Xi'an Jiaotong University  ·  SGIT AI Lab  ·  Zhejiang University of Technology  ·  Baidu

^† Corresponding authors

D2DF is a three-stage framework for one-step video object removal. It first distills a multi-step draft-guided diffusion teacher into D2DF-DG, a one-step draft refiner, through Prior-Privileged Consistency Distillation (PPCD). It then uses the lightweight Self-Guided Fast Planting (SGFP) module to construct pseudo-drafts in latent space, producing D2DF-DF, a fully draft-free one-step generator.

This repository provides standalone inference code for both D2DF-DF and D2DF-DG using the official Hugging Face diffusers and transformers packages.

Installation

git clone https://github.com/bigD233/D2DF.git
cd D2DF
pip install -r requirements.txt

We recommend Python 3.11 and an NVIDIA GPU with CUDA support. The released inference code was tested with PyTorch 2.8, CUDA 12.8, diffusers >= 0.32, and transformers >= 4.44.

Model Weights

Download the base CogVideoX-5B-I2V model from its official Hugging Face repository.

Download the D2DF-DF and D2DF-DG checkpoints from our D2DF Hugging Face repository. After extracting the downloaded archives, pass the corresponding directories to --transformer_path.

The included prompt_embeds.pt stores the pre-computed embedding for the prompt "background", so the T5 encoder does not need to be loaded during normal inference.

Input Format

Prepare the input as directories of numbered image files:

inputs/
├── imgs/
│   ├── 00000.jpg
│   ├── 00001.jpg
│   └── ...
├── mask/
│   ├── 00000.png
│   ├── 00001.png
│   └── ...
└── drafts/                 # required only by D2DF-DG
    ├── 00000.jpg
    ├── 00001.jpg
    └── ...

• The repository includes this ready-to-run 25-frame example under inputs/.
• Frames, masks, and drafts are read in filename order.
• Mask pixels belonging to the removal target should have value 255 by default.
• Inputs are resized to the requested output resolution.
• The default setting uses 25 frames at 480 x 720 resolution.

Inference

D2DF-DF: Draft-Free One-Step Removal

No external draft is required:

python infer.py \
  --mode df \
  --base_model_path <PATH_TO_COGVIDEOX_5B_I2V> \
  --transformer_path <PATH_TO_D2DF_DF> \
  --frames_dir inputs/imgs \
  --mask_dir inputs/mask \
  --output_dir outputs/I-210910_I09032_T04_df \
  --num_frames 25 \
  --height 480 \
  --width 720

D2DF-DG: Draft-Guided One-Step Refinement

Supply a draft produced by a video object removal method such as ProPainter:

python infer.py \
  --mode dg \
  --base_model_path <PATH_TO_COGVIDEOX_5B_I2V> \
  --transformer_path <PATH_TO_D2DF_DG> \
  --frames_dir inputs/imgs \
  --mask_dir inputs/mask \
  --draft_dir inputs/drafts \
  --output_dir outputs/I-210910_I09032_T04_dg \
  --num_frames 25 \
  --height 480 \
  --width 720

The default configuration performs one denoising step with guidance_scale=1.0. The output directory contains an MP4 video and a GIF preview. Add --save_frames to also save individual PNG frames.

Citation

@misc{chen2026d2df,
  title={From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting},
  author={Zizhao Chen and Ping Wei and Guang Dai and Jingdong Wang and Mengmeng Wang},
  year={2026}
}

Acknowledgements

Our implementation is built upon CogVideoX, Hugging Face Diffusers, and ProPainter. We sincerely thank the contributors of these projects for their excellent work.

License

This repository is released under the Apache License 2.0.

Contact

For questions about this work, please contact Zizhao Chen.